pytorch implementation of dftd2 & dftd3

Overview

torch-dftd

pytorch implementation of dftd2 [1] & dftd3 [2, 3]

Install

# Install from pypi
pip install torch-dftd

# Install from source (for developers)
git clone https://github.com/pfnet-research/torch-dftd
pip install -e .

Quick start

from ase.build import molecule
from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator

atoms = molecule("CH3CH2OCH3")
# device="cuda:0" for fast GPU computation.
calc = TorchDFTD3Calculator(atoms=atoms, device="cpu", damping="bj")

energy = atoms.get_potential_energy()
forces = atoms.get_forces()

print(f"energy {energy} eV")
print(f"forces {forces}")

Dependency

The library is tested under following environment.

  • python: 3.6
  • CUDA: 10.2
torch==1.5.1
ase==3.21.1
# Below is only for 3-body term
cupy-cuda102==8.6.0
pytorch-pfn-extras==0.3.2

Development tips

Formatting & Linting

pysen is used to format the python code of this repository.
You can simply run below to get your code formatted :)

# Format the code
$ pysen run format
# Check the code format
$ pysen run lint

CUDA Kernel function implementation with cupy

cupy supports users to implement CUDA kernels within python code, and it can be easily linked with pytorch tensor calculations.
Element wise kernel is implemented and used in some pytorch functions to accelerate speed with GPU.

See document for details about user defined kernel.

Citation

Please always cite original paper of DFT-D2 [1] or DFT-D3 [2, 3], if you used this software for your publication.

DFT-D2:
[1] S. Grimme, J. Comput. Chem, 27 (2006), 1787-1799. DOI: 10.1002/jcc.20495

DFT-D3:
[2] S. Grimme, J. Antony, S. Ehrlich and H. Krieg, J. Chem. Phys, 132 (2010), 154104. DOI: 10.1063/1.3382344

If BJ-damping is used in DFT-D3:
[3] S. Grimme, S. Ehrlich and L. Goerigk, J. Comput. Chem, 32 (2011), 1456-1465. DOI: 10.1002/jcc.21759

Issues
  • update version v0.1.0

    update version v0.1.0

    opened by corochann 11
  • v0.2.0

    v0.2.0

    null

    opened by corochann 2
  • Raise Error with single atom inputs.

    Raise Error with single atom inputs.

    When the length of atoms is 1, the routine raises error.

    from ase.build import molecule
    from ase.calculators.dftd3 import DFTD3
    from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator
    
    if __name__ == "__main__":
        atoms = molecule("H")
        # device="cuda:0" for fast GPU computation.
        calc = TorchDFTD3Calculator(atoms=atoms, device="cpu", damping="bj")
    
        energy = atoms.get_potential_energy()
        forces = atoms.get_forces()
    
        print(f"energy {energy} eV")
        print(f"forces {forces}")
    
    
    Traceback (most recent call last):
      File "quick.py", line 12, in <module>
        energy = atoms.get_potential_energy()
      File "/home/ahayashi/envs/dftd/lib/python3.8/site-packages/ase/atoms.py", line 731, in get_potential_energy
        energy = self._calc.get_potential_energy(self)
      File "/home/ahayashi/envs/dftd/lib/python3.8/site-packages/ase/calculators/calculator.py", line 709, in get_potential_energy
        energy = self.get_property('energy', atoms)
      File "/home/ahayashi/torch-dftd/torch_dftd/torch_dftd3_calculator.py", line 141, in get_property
        dftd3_result = Calculator.get_property(self, name, atoms, allow_calculation)
      File "/home/ahayashi/envs/dftd/lib/python3.8/site-packages/ase/calculators/calculator.py", line 737, in get_property
        self.calculate(atoms, [name], system_changes)
      File "/home/ahayashi/torch-dftd/torch_dftd/torch_dftd3_calculator.py", line 119, in calculate
        results = self.dftd_module.calc_energy(**input_dicts, damping=self.damping)[0]
      File "/home/ahayashi/torch-dftd/torch_dftd/nn/base_dftd_module.py", line 75, in calc_energy
        E_disp = self.calc_energy_batch(
      File "/home/ahayashi/torch-dftd/torch_dftd/nn/dftd3_module.py", line 86, in calc_energy_batch
        E_disp = d3_autoev * edisp(
      File "/home/ahayashi/torch-dftd/torch_dftd/functions/dftd3.py", line 189, in edisp
        c6 = _getc6(Zi, Zj, nci, ncj, c6ab=c6ab, k3=k3)  # c6 coefficients
      File "/home/ahayashi/torch-dftd/torch_dftd/functions/dftd3.py", line 97, in _getc6
        k3_rnc = torch.where(cn0 > 0.0, k3 * r, -1.0e20 * torch.ones_like(r)).view(n_edges, -1)
    RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
    
    opened by AkihideHayashi 1
  • move pysen to develop dependency

    move pysen to develop dependency

    I think we can remove linter tools from prerequisites.

    opened by So-Takamoto 1
  • [WIP] Cell-related gradient modifications

    [WIP] Cell-related gradient modifications

    I found that the current implementation has several performance issues regarding gradient wrt. cell. This PR modifies that. Since the changes are relatively much, I will put some comments.

    Change summary:

    • Use shift for gradient instead of cell.
    • shift is now length scale instead cell unit.
    • Calculate Voigt notation style stress directly

    Also, this PR contains bugfix related to sked cell.

    bug enhancement 
    opened by So-Takamoto 1
  • Add PFP citation in README.md

    Add PFP citation in README.md

    opened by corochann 0
  • Fixed a bug for inputs with 0 adjacencies.

    Fixed a bug for inputs with 0 adjacencies.

    The _gettc6 routine now works correctly even when the number of adjacencies is 0. Instead of calling calc_neighbor_by_pymatgen when the number of atoms is 0 and the periodic boundary condition, it now return edge_index, S for adjacency 0. In my environment, using the result of torch.sum for the size of torch.zeros caused an error, so I changed it to cast the result of sum to int.

    bug 
    opened by AkihideHayashi 0
  • Remove RuntimeError on no-cupy environment

    Remove RuntimeError on no-cupy environment

    ppe.cuda.use_torch_mempool_in_cupy() causes RuntimeError if cupy is not installed, which cannot be caught in the corresponding except line.

    bug 
    opened by So-Takamoto 0
  • Bugfix: batch calculation with abc=True

    Bugfix: batch calculation with abc=True

    I found that test function test_calc_energy_force_stress_device_batch_abc unintentionally ignores abc argument.

    This PR modified related implementation to work it.

    In addition, corner case correspondence when the total number of atom is zero is also added. (n_graphs cannot be calculated from batch_edge when len(batch_edge) == 0.)

    bug 
    opened by So-Takamoto 0
  • Support torch.jit.script

    Support torch.jit.script

    null

    opened by corochann 0
  •  Bug in test for stress

    Bug in test for stress

    In test_torch_dftd3_calculator.py/_assert_energy_force_stress_equal, there is a code below.

        if np.all(atoms.pbc == np.array([True, True, True])):
            s1 = atoms.get_stress()
            s2 = atoms.get_stress()
            assert np.allclose(s1, s2, atol=1e-5, rtol=1e-5)
    

    This code cannot compare the results of stresses of calc1 and calc2. Both s1 and s2 are the stress of calc2.

    opened by AkihideHayashi 0
  • Improve test robustness

    Improve test robustness

    https://github.com/pfnet-research/torch-dftd/pull/3#discussion_r664202835

    random cell assignment may be not robust.

    opened by corochann 0
Releases(v0.3.0)
  • v0.3.0(Sep 21, 2021)

    This is the release note of v0.3.0.

    Highlights

    • use shift for gradient calculation instead of cell #13 (Thank you @So-Takamoto )
      • It includes 1. speed up of stress calculation for batch atoms, and 2. bug fix for stress calculation when cell is skewed.
    Source code(tar.gz)
    Source code(zip)
  • v0.2.0(Sep 4, 2021)

    This is the release note of v0.2.0.

    Highlights

    • Add PFP citation in README.md #2
    • Use pymatgen for pbc neighbor search speed up #3

    Bug fixes

    • Fixed a bug for inputs with 0 adjacencies. #6 (Thank you @AkihideHayashi )
    • Remove RuntimeError on no-cupy environment #8 (Thank you @So-Takamoto )
    • Bugfix: batch calculation with abc=True #9 (Thank you @So-Takamoto )

    Others

    • move pysen to develop dependency #10 (Thank you @So-Takamoto )
    Source code(tar.gz)
    Source code(zip)
  • v0.1.0(May 10, 2021)

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